Overview

Dataset statistics

Number of variables16
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.4 KiB
Average record size in memory130.6 B

Variable types

Text3
Numeric11
Categorical2

Alerts

danceability is highly overall correlated with time_signatureHigh correlation
energy is highly overall correlated with loudnessHigh correlation
loudness is highly overall correlated with energyHigh correlation
speechiness is highly overall correlated with time_signatureHigh correlation
tempo is highly overall correlated with time_signatureHigh correlation
time_signature is highly overall correlated with danceability and 2 other fieldsHigh correlation
time_signature is highly imbalanced (75.8%)Imbalance
Track_ID has unique valuesUnique
Track_Name has unique valuesUnique
loudness has unique valuesUnique
tempo has unique valuesUnique
duration_ms has unique valuesUnique
key has 1 (2.0%) zerosZeros
instrumentalness has 34 (68.0%) zerosZeros

Reproduction

Analysis started2023-11-24 08:07:50.275676
Analysis finished2023-11-24 08:08:13.688223
Duration23.41 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Track_ID
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-11-24T11:08:13.929764image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1100
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st row6Es8Sk3xe1HiJ2MXCfHHwR
2nd row7aPsseax6rNFyipHn9A5CR
3rd row7HrgGbnNKZKhBL70aFKpXk
4th row4sl3IoHskl2Kbdy27gZ4yn
5th row3cDyocF0Ibdfs1SFQk5cLZ
ValueCountFrequency (%)
6es8sk3xe1hij2mxcfhhwr 1
 
2.0%
4sw9ghnw8nfkodqmh0ij45 1
 
2.0%
3onnzh6hmqigihj1nchlrh 1
 
2.0%
7hrggbnnkzkhbl70afkpxk 1
 
2.0%
4sl3iohskl2kbdy27gz4yn 1
 
2.0%
3cdyocf0ibdfs1sfqk5clz 1
 
2.0%
2hafqojbgxdtjwcovnef14 1
 
2.0%
6hwgwcfcwhosjqw7aypequ 1
 
2.0%
5f4moetlngammdtjnbf9s7 1
 
2.0%
7iqxytyug13aoehxgg28nh 1
 
2.0%
Other values (40) 40
80.0%
2023-11-24T11:08:14.461730image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 39
 
3.5%
1 25
 
2.3%
b 24
 
2.2%
2 24
 
2.2%
6 23
 
2.1%
m 23
 
2.1%
h 23
 
2.1%
O 23
 
2.1%
j 23
 
2.1%
s 23
 
2.1%
Other values (52) 850
77.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 461
41.9%
Uppercase Letter 416
37.8%
Decimal Number 223
20.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 24
 
5.2%
m 23
 
5.0%
h 23
 
5.0%
j 23
 
5.0%
s 23
 
5.0%
y 22
 
4.8%
i 22
 
4.8%
a 20
 
4.3%
f 20
 
4.3%
d 19
 
4.1%
Other values (16) 242
52.5%
Uppercase Letter
ValueCountFrequency (%)
O 23
 
5.5%
F 22
 
5.3%
M 20
 
4.8%
N 20
 
4.8%
S 18
 
4.3%
H 18
 
4.3%
G 17
 
4.1%
J 17
 
4.1%
X 17
 
4.1%
C 17
 
4.1%
Other values (16) 227
54.6%
Decimal Number
ValueCountFrequency (%)
7 39
17.5%
1 25
11.2%
2 24
10.8%
6 23
10.3%
3 22
9.9%
0 22
9.9%
5 18
8.1%
8 18
8.1%
4 17
7.6%
9 15
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 877
79.7%
Common 223
 
20.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 24
 
2.7%
m 23
 
2.6%
h 23
 
2.6%
O 23
 
2.6%
j 23
 
2.6%
s 23
 
2.6%
y 22
 
2.5%
F 22
 
2.5%
i 22
 
2.5%
a 20
 
2.3%
Other values (42) 652
74.3%
Common
ValueCountFrequency (%)
7 39
17.5%
1 25
11.2%
2 24
10.8%
6 23
10.3%
3 22
9.9%
0 22
9.9%
5 18
8.1%
8 18
8.1%
4 17
7.6%
9 15
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 39
 
3.5%
1 25
 
2.3%
b 24
 
2.2%
2 24
 
2.2%
6 23
 
2.1%
m 23
 
2.1%
h 23
 
2.1%
O 23
 
2.1%
j 23
 
2.1%
s 23
 
2.1%
Other values (52) 850
77.3%

Track_Name
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-11-24T11:08:14.833168image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length36
Median length22
Mean length13.74
Min length4

Characters and Unicode

Total characters687
Distinct characters71
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st rowLa_Original.mp3
2nd rowLa Morocha
3rd rowLinda - Remix
4th rowUna Foto
5th rowLollipop
ValueCountFrequency (%)
8
 
5.9%
crossover 4
 
3.0%
no 4
 
3.0%
el 3
 
2.2%
en 3
 
2.2%
me 2
 
1.5%
una 2
 
1.5%
la 2
 
1.5%
ahi 2
 
1.5%
ni 2
 
1.5%
Other values (99) 103
76.3%
2023-11-24T11:08:15.454289image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
85
 
12.4%
a 36
 
5.2%
o 32
 
4.7%
O 31
 
4.5%
i 31
 
4.5%
E 30
 
4.4%
s 25
 
3.6%
e 24
 
3.5%
R 22
 
3.2%
L 22
 
3.2%
Other values (61) 349
50.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 280
40.8%
Uppercase Letter 276
40.2%
Space Separator 85
 
12.4%
Other Punctuation 16
 
2.3%
Decimal Number 16
 
2.3%
Math Symbol 5
 
0.7%
Connector Punctuation 3
 
0.4%
Open Punctuation 2
 
0.3%
Close Punctuation 2
 
0.3%
Dash Punctuation 2
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 31
11.2%
E 30
10.9%
R 22
 
8.0%
L 22
 
8.0%
S 21
 
7.6%
A 21
 
7.6%
N 15
 
5.4%
M 14
 
5.1%
C 13
 
4.7%
V 13
 
4.7%
Other values (17) 74
26.8%
Lowercase Letter
ValueCountFrequency (%)
a 36
12.9%
o 32
11.4%
i 31
11.1%
s 25
8.9%
e 24
 
8.6%
n 18
 
6.4%
l 17
 
6.1%
r 15
 
5.4%
c 11
 
3.9%
t 10
 
3.6%
Other values (16) 61
21.8%
Decimal Number
ValueCountFrequency (%)
3 5
31.2%
1 4
25.0%
5 2
 
12.5%
2 2
 
12.5%
4 1
 
6.2%
0 1
 
6.2%
7 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 7
43.8%
# 4
25.0%
* 3
18.8%
: 1
 
6.2%
, 1
 
6.2%
Space Separator
ValueCountFrequency (%)
85
100.0%
Math Symbol
ValueCountFrequency (%)
| 5
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 556
80.9%
Common 131
 
19.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 36
 
6.5%
o 32
 
5.8%
O 31
 
5.6%
i 31
 
5.6%
E 30
 
5.4%
s 25
 
4.5%
e 24
 
4.3%
R 22
 
4.0%
L 22
 
4.0%
S 21
 
3.8%
Other values (43) 282
50.7%
Common
ValueCountFrequency (%)
85
64.9%
. 7
 
5.3%
| 5
 
3.8%
3 5
 
3.8%
1 4
 
3.1%
# 4
 
3.1%
_ 3
 
2.3%
* 3
 
2.3%
( 2
 
1.5%
) 2
 
1.5%
Other values (8) 11
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 679
98.8%
None 8
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
85
 
12.5%
a 36
 
5.3%
o 32
 
4.7%
O 31
 
4.6%
i 31
 
4.6%
E 30
 
4.4%
s 25
 
3.7%
e 24
 
3.5%
R 22
 
3.2%
L 22
 
3.2%
Other values (55) 341
50.2%
None
ValueCountFrequency (%)
Ó 2
25.0%
á 2
25.0%
Á 1
12.5%
Í 1
12.5%
ó 1
12.5%
é 1
12.5%
Distinct44
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-11-24T11:08:15.841827image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length94
Median length41
Mean length26.98
Min length6

Characters and Unicode

Total characters1349
Distinct characters58
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)80.0%

Sample

1st row['Emilia', 'TINI']
2nd row['Luck Ra', 'BM']
3rd row['Marka Akme', 'DJ Tao', 'Lauty Gram', 'Migrantes', 'Peipper']
4th row['Mesita']
5th row['Darell']
ValueCountFrequency (%)
j 7
 
3.9%
emilia 7
 
3.9%
milo 7
 
3.9%
bm 5
 
2.8%
maria 5
 
2.8%
becerra 5
 
2.8%
one 5
 
2.8%
big 5
 
2.8%
luck 4
 
2.2%
personajes 4
 
2.2%
Other values (93) 127
70.2%
2023-11-24T11:08:16.466466image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 216
16.0%
131
 
9.7%
a 83
 
6.2%
e 79
 
5.9%
i 70
 
5.2%
, 59
 
4.4%
r 51
 
3.8%
] 50
 
3.7%
[ 50
 
3.7%
o 45
 
3.3%
Other values (48) 515
38.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 621
46.0%
Other Punctuation 279
20.7%
Uppercase Letter 216
 
16.0%
Space Separator 131
 
9.7%
Close Punctuation 50
 
3.7%
Open Punctuation 50
 
3.7%
Other Letter 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 83
13.4%
e 79
12.7%
i 70
11.3%
r 51
 
8.2%
o 45
 
7.2%
n 40
 
6.4%
l 37
 
6.0%
s 33
 
5.3%
u 27
 
4.3%
c 18
 
2.9%
Other values (16) 138
22.2%
Uppercase Letter
ValueCountFrequency (%)
M 30
13.9%
B 26
 
12.0%
L 18
 
8.3%
A 13
 
6.0%
P 12
 
5.6%
I 11
 
5.1%
K 11
 
5.1%
O 10
 
4.6%
R 10
 
4.6%
E 9
 
4.2%
Other values (14) 66
30.6%
Other Punctuation
ValueCountFrequency (%)
' 216
77.4%
, 59
 
21.1%
" 4
 
1.4%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
131
100.0%
Close Punctuation
ValueCountFrequency (%)
] 50
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 837
62.0%
Common 510
37.8%
Han 2
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 83
 
9.9%
e 79
 
9.4%
i 70
 
8.4%
r 51
 
6.1%
o 45
 
5.4%
n 40
 
4.8%
l 37
 
4.4%
s 33
 
3.9%
M 30
 
3.6%
u 27
 
3.2%
Other values (40) 342
40.9%
Common
ValueCountFrequency (%)
' 216
42.4%
131
25.7%
, 59
 
11.6%
] 50
 
9.8%
[ 50
 
9.8%
" 4
 
0.8%
Han
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1345
99.7%
None 2
 
0.1%
CJK 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 216
16.1%
131
 
9.7%
a 83
 
6.2%
e 79
 
5.9%
i 70
 
5.2%
, 59
 
4.4%
r 51
 
3.8%
] 50
 
3.7%
[ 50
 
3.7%
o 45
 
3.3%
Other values (44) 511
38.0%
None
ValueCountFrequency (%)
Á 1
50.0%
ñ 1
50.0%
CJK
ValueCountFrequency (%)
1
50.0%
1
50.0%

danceability
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75146
Minimum0.499
Maximum0.911
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:08:16.726273image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.499
5-th percentile0.56985
Q10.715
median0.766
Q30.81975
95-th percentile0.85855
Maximum0.911
Range0.412
Interquartile range (IQR)0.10475

Descriptive statistics

Standard deviation0.091994944
Coefficient of variation (CV)0.12242161
Kurtosis0.43479276
Mean0.75146
Median Absolute Deviation (MAD)0.0545
Skewness-0.87232609
Sum37.573
Variance0.0084630698
MonotonicityNot monotonic
2023-11-24T11:08:16.973024image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0.73 3
 
6.0%
0.822 2
 
4.0%
0.844 2
 
4.0%
0.781 2
 
4.0%
0.736 2
 
4.0%
0.833 1
 
2.0%
0.667 1
 
2.0%
0.813 1
 
2.0%
0.838 1
 
2.0%
0.787 1
 
2.0%
Other values (34) 34
68.0%
ValueCountFrequency (%)
0.499 1
2.0%
0.537 1
2.0%
0.564 1
2.0%
0.577 1
2.0%
0.606 1
2.0%
0.618 1
2.0%
0.623 1
2.0%
0.667 1
2.0%
0.668 1
2.0%
0.681 1
2.0%
ValueCountFrequency (%)
0.911 1
2.0%
0.882 1
2.0%
0.859 1
2.0%
0.858 1
2.0%
0.845 1
2.0%
0.844 2
4.0%
0.841 1
2.0%
0.838 1
2.0%
0.833 1
2.0%
0.831 1
2.0%

energy
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.67296
Minimum0.402
Maximum0.936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:08:17.209425image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.402
5-th percentile0.4329
Q10.59875
median0.668
Q30.776
95-th percentile0.8856
Maximum0.936
Range0.534
Interquartile range (IQR)0.17725

Descriptive statistics

Standard deviation0.13274357
Coefficient of variation (CV)0.19725328
Kurtosis-0.50543279
Mean0.67296
Median Absolute Deviation (MAD)0.0995
Skewness-0.096695779
Sum33.648
Variance0.017620856
MonotonicityNot monotonic
2023-11-24T11:08:17.510504image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.519 2
 
4.0%
0.607 2
 
4.0%
0.656 1
 
2.0%
0.737 1
 
2.0%
0.682 1
 
2.0%
0.708 1
 
2.0%
0.621 1
 
2.0%
0.765 1
 
2.0%
0.68 1
 
2.0%
0.423 1
 
2.0%
Other values (38) 38
76.0%
ValueCountFrequency (%)
0.402 1
2.0%
0.421 1
2.0%
0.423 1
2.0%
0.445 1
2.0%
0.512 1
2.0%
0.513 1
2.0%
0.517 1
2.0%
0.519 2
4.0%
0.553 1
2.0%
0.555 1
2.0%
ValueCountFrequency (%)
0.936 1
2.0%
0.929 1
2.0%
0.9 1
2.0%
0.868 1
2.0%
0.854 1
2.0%
0.827 1
2.0%
0.809 1
2.0%
0.795 1
2.0%
0.793 1
2.0%
0.792 1
2.0%

key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.14
Minimum0
Maximum11
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:08:17.728076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median6.5
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4464415
Coefficient of variation (CV)0.56130969
Kurtosis-1.1738941
Mean6.14
Median Absolute Deviation (MAD)2.5
Skewness-0.17946327
Sum307
Variance11.877959
MonotonicityNot monotonic
2023-11-24T11:08:17.933560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 7
14.0%
1 7
14.0%
5 6
12.0%
7 5
10.0%
9 5
10.0%
8 5
10.0%
4 4
8.0%
6 3
6.0%
2 3
6.0%
10 3
6.0%
Other values (2) 2
 
4.0%
ValueCountFrequency (%)
0 1
 
2.0%
1 7
14.0%
2 3
6.0%
3 1
 
2.0%
4 4
8.0%
5 6
12.0%
6 3
6.0%
7 5
10.0%
8 5
10.0%
9 5
10.0%
ValueCountFrequency (%)
11 7
14.0%
10 3
6.0%
9 5
10.0%
8 5
10.0%
7 5
10.0%
6 3
6.0%
5 6
12.0%
4 4
8.0%
3 1
 
2.0%
2 3
6.0%

loudness
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.39714
Minimum-10.957
Maximum-1.669
Zeros0
Zeros (%)0.0%
Negative50
Negative (%)100.0%
Memory size532.0 B
2023-11-24T11:08:18.201138image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-10.957
5-th percentile-9.2172
Q1-6.47575
median-5
Q3-4.0285
95-th percentile-2.58785
Maximum-1.669
Range9.288
Interquartile range (IQR)2.44725

Descriptive statistics

Standard deviation2.1453863
Coefficient of variation (CV)-0.3975043
Kurtosis0.3516227
Mean-5.39714
Median Absolute Deviation (MAD)1.229
Skewness-0.79132427
Sum-269.857
Variance4.6026826
MonotonicityNot monotonic
2023-11-24T11:08:18.489039image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.751 1
 
2.0%
-2.628 1
 
2.0%
-4.013 1
 
2.0%
-4.045 1
 
2.0%
-3.598 1
 
2.0%
-4.668 1
 
2.0%
-5.009 1
 
2.0%
-4.184 1
 
2.0%
-6.085 1
 
2.0%
-10.953 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
-10.957 1
2.0%
-10.953 1
2.0%
-9.624 1
2.0%
-8.72 1
2.0%
-8.496 1
2.0%
-8.205 1
2.0%
-8.199 1
2.0%
-7.95 1
2.0%
-7.653 1
2.0%
-7.455 1
2.0%
ValueCountFrequency (%)
-1.669 1
2.0%
-2.248 1
2.0%
-2.555 1
2.0%
-2.628 1
2.0%
-2.751 1
2.0%
-2.978 1
2.0%
-3.236 1
2.0%
-3.502 1
2.0%
-3.51 1
2.0%
-3.598 1
2.0%

mode
Categorical

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
0
26 
1
24 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 26
52.0%
1 24
48.0%

Length

2023-11-24T11:08:18.754666image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T11:08:18.922513image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 26
52.0%
1 24
48.0%

Most occurring characters

ValueCountFrequency (%)
0 26
52.0%
1 24
48.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26
52.0%
1 24
48.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26
52.0%
1 24
48.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26
52.0%
1 24
48.0%

speechiness
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.103164
Minimum0.0285
Maximum0.431
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:08:19.131362image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.0285
5-th percentile0.0381
Q10.050525
median0.06635
Q30.11625
95-th percentile0.2899
Maximum0.431
Range0.4025
Interquartile range (IQR)0.065725

Descriptive statistics

Standard deviation0.086419756
Coefficient of variation (CV)0.83769295
Kurtosis4.2343825
Mean0.103164
Median Absolute Deviation (MAD)0.0245
Skewness2.0698012
Sum5.1582
Variance0.0074683742
MonotonicityNot monotonic
2023-11-24T11:08:19.411867image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.0381 2
 
4.0%
0.0547 2
 
4.0%
0.107 2
 
4.0%
0.0825 2
 
4.0%
0.0391 1
 
2.0%
0.0493 1
 
2.0%
0.0436 1
 
2.0%
0.0402 1
 
2.0%
0.166 1
 
2.0%
0.068 1
 
2.0%
Other values (36) 36
72.0%
ValueCountFrequency (%)
0.0285 1
2.0%
0.0341 1
2.0%
0.0381 2
4.0%
0.0391 1
2.0%
0.0402 1
2.0%
0.0435 1
2.0%
0.0436 1
2.0%
0.045 1
2.0%
0.0454 1
2.0%
0.0459 1
2.0%
ValueCountFrequency (%)
0.431 1
2.0%
0.329 1
2.0%
0.307 1
2.0%
0.269 1
2.0%
0.262 1
2.0%
0.244 1
2.0%
0.166 1
2.0%
0.159 1
2.0%
0.155 1
2.0%
0.145 1
2.0%

acousticness
Real number (ℝ)

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2490814
Minimum0.00362
Maximum0.827
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:08:19.685472image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.00362
5-th percentile0.012
Q10.0929
median0.2015
Q30.372
95-th percentile0.52385
Maximum0.827
Range0.82338
Interquartile range (IQR)0.2791

Descriptive statistics

Standard deviation0.18235522
Coefficient of variation (CV)0.73211095
Kurtosis0.43181766
Mean0.2490814
Median Absolute Deviation (MAD)0.12505
Skewness0.78901338
Sum12.45407
Variance0.033253427
MonotonicityNot monotonic
2023-11-24T11:08:19.935098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.343 2
 
4.0%
0.012 2
 
4.0%
0.0836 1
 
2.0%
0.48 1
 
2.0%
0.163 1
 
2.0%
0.0739 1
 
2.0%
0.107 1
 
2.0%
0.176 1
 
2.0%
0.15 1
 
2.0%
0.374 1
 
2.0%
Other values (38) 38
76.0%
ValueCountFrequency (%)
0.00362 1
2.0%
0.00705 1
2.0%
0.012 2
4.0%
0.0605 1
2.0%
0.0613 1
2.0%
0.0739 1
2.0%
0.079 1
2.0%
0.0836 1
2.0%
0.0887 1
2.0%
0.0892 1
2.0%
ValueCountFrequency (%)
0.827 1
2.0%
0.57 1
2.0%
0.527 1
2.0%
0.52 1
2.0%
0.513 1
2.0%
0.494 1
2.0%
0.48 1
2.0%
0.464 1
2.0%
0.44 1
2.0%
0.396 1
2.0%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.000100134
Minimum0
Maximum0.00214
Zeros34
Zeros (%)68.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:08:20.151495image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.205 × 10-6
95-th percentile0.0004812
Maximum0.00214
Range0.00214
Interquartile range (IQR)4.205 × 10-6

Descriptive statistics

Standard deviation0.00038267605
Coefficient of variation (CV)3.8216395
Kurtosis21.22734
Mean0.000100134
Median Absolute Deviation (MAD)0
Skewness4.5804637
Sum0.0050067
Variance1.4644096 × 10-7
MonotonicityNot monotonic
2023-11-24T11:08:20.344966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 34
68.0%
2.13 × 10-51
 
2.0%
6.25 × 10-51
 
2.0%
3.83 × 10-51
 
2.0%
0.000546 1
 
2.0%
4.66 × 10-61
 
2.0%
0.000402 1
 
2.0%
0.00162 1
 
2.0%
2.05 × 10-61
 
2.0%
1.46 × 10-51
 
2.0%
Other values (7) 7
 
14.0%
ValueCountFrequency (%)
0 34
68.0%
1.45 × 10-61
 
2.0%
2.05 × 10-61
 
2.0%
2.84 × 10-61
 
2.0%
4.66 × 10-61
 
2.0%
1.46 × 10-51
 
2.0%
1.78 × 10-51
 
2.0%
2.13 × 10-51
 
2.0%
2.16 × 10-51
 
2.0%
3.83 × 10-51
 
2.0%
ValueCountFrequency (%)
0.00214 1
2.0%
0.00162 1
2.0%
0.000546 1
2.0%
0.000402 1
2.0%
6.42 × 10-51
2.0%
6.25 × 10-51
2.0%
4.74 × 10-51
2.0%
3.83 × 10-51
2.0%
2.16 × 10-51
2.0%
2.13 × 10-51
2.0%

liveness
Real number (ℝ)

Distinct45
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.194348
Minimum0.0599
Maximum0.688
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:08:20.557007image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.0599
5-th percentile0.08176
Q10.10725
median0.1395
Q30.24675
95-th percentile0.4729
Maximum0.688
Range0.6281
Interquartile range (IQR)0.1395

Descriptive statistics

Standard deviation0.13562196
Coefficient of variation (CV)0.69783051
Kurtosis3.8611709
Mean0.194348
Median Absolute Deviation (MAD)0.05395
Skewness1.889237
Sum9.7174
Variance0.018393317
MonotonicityNot monotonic
2023-11-24T11:08:20.798800image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0.112 3
 
6.0%
0.108 3
 
6.0%
0.202 2
 
4.0%
0.18 1
 
2.0%
0.134 1
 
2.0%
0.0955 1
 
2.0%
0.343 1
 
2.0%
0.58 1
 
2.0%
0.553 1
 
2.0%
0.247 1
 
2.0%
Other values (35) 35
70.0%
ValueCountFrequency (%)
0.0599 1
2.0%
0.0699 1
2.0%
0.0814 1
2.0%
0.0822 1
2.0%
0.083 1
2.0%
0.0842 1
2.0%
0.0869 1
2.0%
0.0872 1
2.0%
0.0892 1
2.0%
0.095 1
2.0%
ValueCountFrequency (%)
0.688 1
2.0%
0.58 1
2.0%
0.553 1
2.0%
0.375 1
2.0%
0.355 1
2.0%
0.343 1
2.0%
0.338 1
2.0%
0.308 1
2.0%
0.295 1
2.0%
0.279 1
2.0%

valence
Real number (ℝ)

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64864
Minimum0.13
Maximum0.971
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:08:21.052320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile0.30045
Q10.518
median0.6765
Q30.79875
95-th percentile0.9483
Maximum0.971
Range0.841
Interquartile range (IQR)0.28075

Descriptive statistics

Standard deviation0.20478937
Coefficient of variation (CV)0.31572115
Kurtosis-0.32899515
Mean0.64864
Median Absolute Deviation (MAD)0.145
Skewness-0.40688595
Sum32.432
Variance0.041938684
MonotonicityNot monotonic
2023-11-24T11:08:21.292796image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.7 2
 
4.0%
0.905 1
 
2.0%
0.698 1
 
2.0%
0.688 1
 
2.0%
0.607 1
 
2.0%
0.634 1
 
2.0%
0.96 1
 
2.0%
0.13 1
 
2.0%
0.514 1
 
2.0%
0.619 1
 
2.0%
Other values (39) 39
78.0%
ValueCountFrequency (%)
0.13 1
2.0%
0.244 1
2.0%
0.264 1
2.0%
0.345 1
2.0%
0.346 1
2.0%
0.38 1
2.0%
0.393 1
2.0%
0.4 1
2.0%
0.441 1
2.0%
0.484 1
2.0%
ValueCountFrequency (%)
0.971 1
2.0%
0.966 1
2.0%
0.96 1
2.0%
0.934 1
2.0%
0.923 1
2.0%
0.91 1
2.0%
0.906 1
2.0%
0.905 1
2.0%
0.853 1
2.0%
0.848 1
2.0%

tempo
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.78414
Minimum81.869
Maximum192.147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:08:21.531676image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum81.869
5-th percentile88.00395
Q193.50575
median100.525
Q3136.38825
95-th percentile165.5692
Maximum192.147
Range110.278
Interquartile range (IQR)42.8825

Descriptive statistics

Standard deviation28.405266
Coefficient of variation (CV)0.24532951
Kurtosis-0.21337209
Mean115.78414
Median Absolute Deviation (MAD)12.49
Skewness0.91030751
Sum5789.207
Variance806.85914
MonotonicityNot monotonic
2023-11-24T11:08:21.772862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.055 1
 
2.0%
137.829 1
 
2.0%
88.01 1
 
2.0%
91.986 1
 
2.0%
120.011 1
 
2.0%
119.984 1
 
2.0%
139.056 1
 
2.0%
98.012 1
 
2.0%
100.109 1
 
2.0%
100.941 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
81.869 1
2.0%
85.962 1
2.0%
87.999 1
2.0%
88.01 1
2.0%
88.06 1
2.0%
89.896 1
2.0%
89.911 1
2.0%
89.946 1
2.0%
89.962 1
2.0%
89.972 1
2.0%
ValueCountFrequency (%)
192.147 1
2.0%
177.939 1
2.0%
169.918 1
2.0%
160.254 1
2.0%
160.02 1
2.0%
159.806 1
2.0%
149.987 1
2.0%
149.947 1
2.0%
149.144 1
2.0%
148.038 1
2.0%

duration_ms
Real number (ℝ)

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179023.76
Minimum120600
Maximum338000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size532.0 B
2023-11-24T11:08:22.090259image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum120600
5-th percentile130750.6
Q1154331.5
median169325.5
Q3197703.25
95-th percentile244139.1
Maximum338000
Range217400
Interquartile range (IQR)43371.75

Descriptive statistics

Standard deviation40498.578
Coefficient of variation (CV)0.22621901
Kurtosis3.7551885
Mean179023.76
Median Absolute Deviation (MAD)20729.5
Skewness1.5079991
Sum8951188
Variance1.6401348 × 109
MonotonicityNot monotonic
2023-11-24T11:08:22.328619image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140625 1
 
2.0%
157830 1
 
2.0%
184587 1
 
2.0%
197920 1
 
2.0%
338000 1
 
2.0%
128677 1
 
2.0%
267194 1
 
2.0%
160856 1
 
2.0%
164101 1
 
2.0%
143998 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
120600 1
2.0%
122500 1
2.0%
128677 1
2.0%
133285 1
2.0%
133969 1
2.0%
134144 1
2.0%
140625 1
2.0%
143936 1
2.0%
143998 1
2.0%
150829 1
2.0%
ValueCountFrequency (%)
338000 1
2.0%
267194 1
2.0%
252348 1
2.0%
234106 1
2.0%
231693 1
2.0%
228629 1
2.0%
225661 1
2.0%
221601 1
2.0%
204481 1
2.0%
204000 1
2.0%

time_signature
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
4
48 
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters50
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 48
96.0%
3 2
 
4.0%

Length

2023-11-24T11:08:22.549360image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-24T11:08:22.711317image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
4 48
96.0%
3 2
 
4.0%

Most occurring characters

ValueCountFrequency (%)
4 48
96.0%
3 2
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 48
96.0%
3 2
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 48
96.0%
3 2
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 48
96.0%
3 2
 
4.0%

Interactions

2023-11-24T11:08:11.011405image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:50.818233image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:52.689537image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:54.748325image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:56.419857image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:58.230915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:00.247845image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:02.207783image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:04.458173image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:06.946333image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:08.800648image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:11.157332image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:50.969405image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:52.846940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:54.890297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:56.565093image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:58.400593image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:00.504964image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:02.418441image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:04.621938image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:07.097948image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:08.952133image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:11.301546image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:51.169216image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:53.016224image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:55.075773image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:56.721493image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:58.576626image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:00.695291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:02.654296image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:04.779621image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:07.253516image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:09.106368image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:11.506261image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:51.361104image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:53.156679image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:55.229846image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:56.864816image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:58.745942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:00.843507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:02.830299image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:05.111382image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:07.399099image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:09.283951image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:11.752817image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:51.593902image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:53.294926image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:55.378394image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:56.997040image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:58.934075image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:00.996730image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:02.981243image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:05.362726image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:07.535869image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:09.554321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:11.996871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:51.776050image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:53.474226image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:55.537589image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:57.183318image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:59.166800image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:01.164055image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:03.152534image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:05.583820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:07.778194image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:09.759279image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:12.213978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:51.920271image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:53.642904image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:55.691490image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:57.325801image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:59.352304image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:01.483786image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:03.292723image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:05.755442image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:07.982071image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:09.963913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:12.416220image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:52.121843image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:53.811806image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:55.852546image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:57.489733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:59.532690image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:01.635073image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:03.472371image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:06.004108image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:08.192896image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:10.114379image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:12.611092image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:52.283675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:54.021059image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:56.008138image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:57.657137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:59.728540image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:01.784431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:03.702358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:06.385681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:08.352153image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:10.358674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:12.789598image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:52.416226image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:54.299624image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:56.148805image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:57.824157image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:59.880738image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:01.926572image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:04.093771image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:06.616301image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:08.497658image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:10.511163image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:12.930099image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:52.557170image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:54.591407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:56.289095image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:07:58.064354image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:00.067013image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:02.069067image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:04.265168image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:06.783007image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:08.653650image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-24T11:08:10.700817image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-24T11:08:22.843371image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
acousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempotime_signaturevalence
acousticness1.000-0.129-0.001-0.1200.001-0.1540.137-0.1960.0000.203-0.2840.0000.104
danceability-0.1291.000-0.0220.1110.091-0.083-0.0950.1720.2450.304-0.1060.5960.003
duration_ms-0.001-0.0221.0000.1260.2410.006-0.3010.0910.000-0.155-0.1920.000-0.303
energy-0.1200.1110.1261.0000.164-0.0880.0170.7530.000-0.2350.0470.238-0.081
instrumentalness0.0010.0910.2410.1641.000-0.1400.0020.1000.0000.0770.0240.000-0.324
key-0.154-0.0830.006-0.088-0.1401.000-0.111-0.0710.000-0.089-0.1390.0000.151
liveness0.137-0.095-0.3010.0170.002-0.1111.000-0.1670.1970.0450.3600.0000.087
loudness-0.1960.1720.0910.7530.100-0.071-0.1671.0000.000-0.126-0.1840.000-0.116
mode0.0000.2450.0000.0000.0000.0000.1970.0001.000-0.234-0.2610.0000.144
speechiness0.2030.304-0.155-0.2350.077-0.0890.045-0.126-0.2341.0000.0980.602-0.019
tempo-0.284-0.106-0.1920.0470.024-0.1390.360-0.184-0.2610.0981.0000.692-0.042
time_signature0.0000.5960.0000.2380.0000.0000.0000.0000.0000.6020.6921.0000.092
valence0.1040.003-0.303-0.081-0.3240.1510.087-0.1160.144-0.019-0.0420.0921.000

Missing values

2023-11-24T11:08:13.141051image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-24T11:08:13.536839image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Track_IDTrack_NameTrack_Artistsdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signature
06Es8Sk3xe1HiJ2MXCfHHwRLa_Original.mp3['Emilia', 'TINI']0.8330.8095-2.75100.03810.083600.0000000.11200.905128.0551406254
17aPsseax6rNFyipHn9A5CRLa Morocha['Luck Ra', 'BM']0.7530.7707-5.75110.03410.096600.0000000.27900.971149.1441341444
27HrgGbnNKZKhBL70aFKpXkLinda - Remix['Marka Akme', 'DJ Tao', 'Lauty Gram', 'Migrantes', 'Peipper']0.7800.6309-4.71910.10500.307000.0000000.18000.78987.9992341064
34sl3IoHskl2Kbdy27gZ4ynUna Foto['Mesita']0.8110.6496-5.08910.11100.343000.0000000.12100.75696.0761225004
43cDyocF0Ibdfs1SFQk5cLZLollipop['Darell']0.8220.7116-4.94310.06680.179000.0000150.08300.934102.9841999474
52HafqoJbgXdtjwCOvNEF14Si No Estás['iñigo quintero']0.5370.4215-8.72010.02850.827000.0000000.13800.52498.2241840614
66hwGwCfCwHoSJQw7AYPEQuANDO['Jere Klein', 'Gittobeatz']0.8440.5988-7.95000.06160.003620.0021400.08690.39398.0221727684
75f4mOETLngamMDTJnbF9s7Exclusive.mp3['Emilia']0.8580.6778-4.29410.11800.106000.0000000.24600.584100.0461206004
87iQXYTyuG13aoeHxGG28NhPERRO NEGRO['Bad Bunny', 'Feid']0.9110.7785-2.24810.26200.088700.0000220.17900.34596.0571627684
90H9WU0OIXPpbOVgzzOanXbNi Una Ni Dos['BM']0.7810.6017-5.81710.04590.513000.0000000.26800.85388.0601615914
Track_IDTrack_NameTrack_Artistsdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signature
402df5CkXBJO7jTGXm54dFJyNo_Se_Ve.mp3['Emilia', 'LUDMILLA', 'Zecca']0.7650.65911-5.05900.08250.08920.0000380.14900.567132.0662036364
416CIMoDfTsvFVGhFi3v9IznLuck Ra | Mission 15['Alan Gomez', 'Luck Ra']0.6910.7284-5.72200.09860.17700.0000000.17100.814160.0201332854
426abr2lKyMZTy21gSPWm7aGEntre Beso y Beso['Ke Personajes']0.7290.86810-1.66900.05360.57000.0000000.20600.90685.9621534884
430aPs3kmiqmt9QG7gWXdefANo soy Eterno['Bizarrap', 'Milo j']0.5640.4022-6.48800.32900.44000.0000630.30800.628121.4501529034
445w7RsSgxxBwdVTSyQDj8BiDISCOTEKA (feat. Locura Mix)['The La Planta', 'BM', 'Alejo Isakk', 'Locura Mix']0.8220.5199-7.18310.13600.17800.0000000.08140.77989.9622044814
456XSqqQIy7Lm7SnwxS4NrGxClassy 101['Feid', 'Young Miko']0.8590.65811-4.79010.15900.14500.0000000.12000.672100.0651959874
463WndNMJo029mMO64l9hGFmDale Mecha['Mesita']0.6230.6364-4.36000.43100.30000.0000000.10600.441146.2861508293
477w7BrPbOjF5OxChs2dxFveEn La Intimidad | CROSSOVER #1['Big One', 'Emilia', 'Callejero Fino']0.8040.5551-4.99110.10700.09240.0000000.06990.64089.9461721184
482kz1YOhJiNubjigR1gB1VXDILUVIO['Rauw Alejandro']0.8450.7959-3.50200.06810.34300.0000180.22400.68194.9691970534
496irysuQyZWd7Bjj7ePiOs2Holanda['Jhayco']0.8310.7591-3.62700.05020.27700.0000000.12500.244107.9612316934